• DocumentCode
    642648
  • Title

    STDP-enabled learning on a reconfigurable neuromorphic platform

  • Author

    Nease, S. ; Brink, Stephen ; Hasler, P.

  • Author_Institution
    Sch. of Electr. & Comput. Eng., Georgia Inst. of Technol., Atlanta, GA, USA
  • fYear
    2013
  • fDate
    8-12 Sept. 2013
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    Spike-Timing Dependent Plasticity (STDP) is a well-known mechanism that implements learning in biological neural networks. We have developed a neuromorphic integrated circuit which contains 100 neurons and 30,000 synapses, 20,000 of which can follow an STDP learning rule. This work presents the initial results for circuits utilizing STDP on this chip.
  • Keywords
    neural chips; plasticity; STDP learning rule; biological neural networks; neuromorphic integrated circuit; reconfigurable neuromorphic platform; spike timing dependent plasticity; Logic gates; Neuromorphics; Neurons; Synchronization; Tunneling; Floating-Gate; Learning; Neuromorphic; STDP;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Circuit Theory and Design (ECCTD), 2013 European Conference on
  • Conference_Location
    Dresden
  • Type

    conf

  • DOI
    10.1109/ECCTD.2013.6662199
  • Filename
    6662199
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=642648